✈️ Flight Price Predictor - End-to-End ML Project

Predict flight ticket prices using machine learning with 10K+ real flight data points

Live Demo • Features • Tech Stack • Installation

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🎯 Project Overview

A production-ready machine learning web application that predicts Indian domestic flight prices based on multiple factors including airline, route, timing, and duration. Built from raw data to deployment with comprehensive feature engineering and model evaluation.

🌟 Key Highlights

• 📊 10,683 flight records analyzed and engineered
• 🧹 34 numerical features extracted from messy categorical/datetime data
• 🎯 5 ML algorithms trained with GridSearchCV hyperparameter tuning
• 🏆 Lasso Regression selected as best model (MAE: ₹1,982.86, R²: 0.6146)
• 🌐 Flask web application with clean, responsive UI
• 📈 End-to-end ML pipeline: Data cleaning → Feature engineering → Model selection → Deployment

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🚀 Features

Data Processing & Feature Engineering

• ✅ Handled missing values and inconsistent data types
• ✅ Extracted temporal features (day, month, year, hour, minute)
• ✅ One-hot encoding for categorical variables (airlines, cities)
• ✅ Duration parsing from string format to numerical hours/minutes
• ✅ Created 34-feature numerical dataset from 11 raw columns

Machine Learning Models

• 🤖 5 Algorithms Trained: Linear Regression, Lasso, Ridge, ElasticNet, SVR
• 🔧 Hyperparameter Tuning: GridSearchCV with 5-fold cross-validation
• 🏆 Best Model: Lasso Regression (MAE: ₹1,982.86, R²: 0.6146)
• 📊 Model comparison and evaluation with MAE, RMSE, and R² metrics
• 💾 Model serialization using pickle for production deployment

Web Application

• 🎨 Modern, responsive UI with gradient design
• ⚡ Real-time price predictions via Flask API
• 🔧 User-friendly form inputs (datetime pickers, dropdowns)
• 📱 Mobile-responsive design

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🛠️ Tech Stack

Category	Technologies
Language	Python 3.12
ML/Data	scikit-learn, pandas, numpy, matplotlib, seaborn
Web Framework	Flask
Data Format	Excel (openpyxl), CSV
Deployment	Gunicorn (production-ready)
Visualization	Matplotlib, Seaborn

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📂 Project Structure

Flight-Price-Predictor/
│
├── datasets/                     # Data folder
│   ├── flight_price.xlsx         # Raw dataset (10,683 rows)
│   └── flight_price_cleaned.csv  # Processed dataset (34 features)
│
├── notebooks/                    # Jupyter notebooks
│   ├── feature_engineering.ipynb # Data cleaning & feature extraction
│   └── model_training.ipynb      # Model training & evaluation
│
├── static/                       # Frontend assets
│   └── style.css                 # UI styling
│
├── templates/                    # Flask HTML templates
│   └── index.html                # Frontend UI template
│
├── application.py                # Flask backend application
├── flight_price_linear_model.pkl # Trained Lasso Regression model (serialized)
├── requirements.txt              # Python dependencies
└── README.md                     # Project documentation

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💡 How It Works

1️⃣ Data Preprocessing

# Original dataset challenges:
- Mixed data types (categorical, datetime, numerical)
- Missing values in Route and Total_Stops
- Inconsistent datetime formats
- String-based duration ("2h 50m")

# Solution:
✓ Parsed dates → Extracted day, month, year
✓ Parsed time → Extracted hour, minute
✓ Converted duration string → Numerical hours + minutes
✓ One-hot encoded 12 airlines, 5 sources, 6 destinations

2️⃣ Feature Engineering

Transformed 11 raw columns into 34 numerical features:

Original	Engineered Features
Airline	12 binary features (one-hot)
Source	5 binary features (one-hot)
Destination	6 binary features (one-hot)
Date_of_Journey	Date, Month, Year (3 features)
Dep_Time	Dep_hour, Dep_minute (2 features)
Arrival_Time	Arrival_hour, Arrival_minute (2 features)
Duration	Duration_hour, Duration_minute (2 features)
Total_Stops	Total_Stops (1 feature)
Price	Target Variable

3️⃣ Model Training & Selection

# Trained 5 different algorithms:
✓ Linear Regression (baseline)
✓ Lasso Regression (L1 regularization)
✓ Ridge Regression (L2 regularization)
✓ ElasticNet (L1 + L2 regularization)
✓ SVR with RBF kernel

# Hyperparameter tuning with GridSearchCV
- 5-fold cross-validation
- Optimized for R² score
- Parameter grids for each algorithm

# Final Selection: Lasso Regression
- MAE: ₹1,982.86 (lowest among all models)
- R²: 0.6146 (explains 61.5% of variance)
- Best alpha: 1.0

4️⃣ Web Application

• User inputs flight details via clean web form
• Flask backend processes inputs into 33-feature array
• Trained model predicts price in rupees
• Result displayed instantly on frontend

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🔧 Installation & Setup

Prerequisites

• Python 3.8 or higher
• pip package manager

Step 1: Clone the Repository

git clone https://github.com/Harsh1574/Flight-Price-Predictor.git
cd Flight-Price-Predictor

Step 2: Create Virtual Environment (Recommended)

python -m venv venv

# On Windows
venv\Scripts\activate

# On macOS/Linux
source venv/bin/activate

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Run the Application

python application.py

Step 5: Access the Web App

Open your browser and navigate to:

http://127.0.0.1:5000/

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📊 Model Performance

Trained and evaluated 5 different regression algorithms with hyperparameter tuning using GridSearchCV:

📈 Model Comparison Summary

Model	MAE (₹)	RMSE (₹)	R² Score	Best Hyperparameters	Status
Lasso Regression	1,982.86	2,855.93	0.6146	alpha: 1.0	✅ Selected
Ridge Regression	~2,000	~2,900	0.6187	alpha: 0.1	⚪ Evaluated
ElasticNet	~2,500	~3,200	0.5544	alpha: 0.1	⚪ Evaluated
SVR (RBF)	3,487.66	4,544.30	0.0242	C: 10, gamma: 0.01	❌ Poor fit
Linear Regression	~2,000	~2,900	~0.61	None (baseline)	⚪ Baseline

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🏆 Winner: Lasso Regression

Final Production Model Metrics:

• Mean Absolute Error (MAE): ₹1,982.86
• Root Mean Squared Error (RMSE): ₹2,855.93
• R² Score: 0.6146 (explains 61.46% of variance)
• Best Hyperparameter: alpha = 1.0

🎯 Model Selection Rationale

Lasso Regression was chosen as the final production model because:

• ✅ Best MAE: Lowest prediction error (₹1,982.86 vs ₹3,487+ for others)
• ✅ Strong R² Score: Explains ~61.5% of price variance
• ✅ Feature Selection: L1 regularization automatically removes irrelevant features
• ✅ Generalization: Regularization prevents overfitting
• ✅ Interpretability: Coefficients show feature importance
• ✅ Fast Predictions: Efficient for real-time web application

📈 Hyperparameter Tuning Details

All models were optimized using GridSearchCV with:

• Cross-Validation: 5-fold CV
• Scoring Metric: R² Score
• Parameter Grids:
  • Lasso/Ridge/ElasticNet: alpha = [0.01, 0.001, 0.1, 1.0, 10.0, 100.0]
  • SVR: C = [0.1, 1, 10], gamma = [0.01, 0.1, 1.0], kernel = ['rbf']

Key Insight: Lasso's L1 regularization with alpha=1.0 provided the optimal trade-off between bias and variance, resulting in the most accurate predictions.

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🎥 Demo

Input Form

Users enter:

• ✈️ Departure date & time
• 🛬 Arrival date & time
• 📍 Source and destination cities
• 🏢 Preferred airline
• 🔢 Number of stops

Output

Estimated Flight Price: ₹8,547.32

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📈 Dataset Information

• Source: Flight price data for Indian domestic routes (2019)
• Size: 10,683 flight records
• Routes: Delhi, Mumbai, Bangalore, Kolkata, Chennai, Cochin, Hyderabad
• Airlines: IndiGo, Air India, Jet Airways, SpiceJet, GoAir, Vistara, Air Asia, TruJet
• Features: Airline, source, destination, departure time, arrival time, duration, stops

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🔑 Key Learnings

Data Engineering

• 🔍 Real-world data is messy: Learned to handle mixed data types, missing values, and inconsistent formats
• 🛠️ Feature extraction is critical: Temporal features (hour, day, month) significantly impact model performance
• 📊 Encoding strategy matters: One-hot encoding transformed categorical data into ML-ready format

Machine Learning

• 🎯 Model selection: Compared multiple algorithms (Linear Regression, SVR, Ridge, Lasso, ElasticNet) for optimal performance
• 📉 Evaluation metrics: Used MAE, RMSE, R² to assess model quality
• 💾 Model persistence: Serialized trained model with pickle for production use

Software Engineering

• 🌐 Full-stack development: Built end-to-end ML application from data to deployment
• 🎨 User experience: Designed clean, intuitive interface for non-technical users
• 🚀 Production readiness: Configured with Gunicorn for scalable deployment

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🚀 Future Enhancements

• Model Improvements

  • Experiment with Random Forest, XGBoost, Neural Networks
  • Hyperparameter tuning with GridSearchCV
  • Feature selection to reduce dimensionality

• Data Expansion

  • Include more recent flight data (2020-2024)
  • Add seasonal trends and holiday pricing
  • Incorporate external factors (fuel prices, demand patterns)

• Application Features

  • Price trend visualization (charts/graphs)
  • Historical price comparison
  • Multi-city route planning
  • Email/SMS price alerts
  • REST API for third-party integration

• Deployment

  • Docker containerization
  • Cloud deployment (AWS/GCP/Heroku)
  • CI/CD pipeline with GitHub Actions
  • Database integration (PostgreSQL/MongoDB)

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🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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👤 Author

Harsvardhan Rajgarhia

CSBS'27, Academy Of Technology

🙏 Acknowledgments

• Dataset sourced from real Indian domestic flight pricing data
• Built as part of continuous learning in Machine Learning and Data Science
• Inspired by real-world pricing prediction challenges in the aviation industry

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⭐ If you found this project helpful, please consider giving it a star!

Made with ❤️ and Python